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All Journal Jurnal Sains dan Teknologi Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Jurnal Pengabdian Magister Pendidikan IPA Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) ABDI MASSA: Jurnal Pengabdian Nasional Indo-MathEdu Intellectuals Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Kreativitas Pada Pengabdian Masyarakat (Krepa) JUAN: Jurnal Pengabdian Nusantara ECONOMIST: Jurnal Ekonomi dan Bisnis Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Pada Masyarakat IPTEKS JAIM: Jurnal Aliansi Ilmu Multidisiplin
I Nyoman Switrayana
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Pengaplikasian Convolutional Neural Network (MobileNetV3) Memanfaatkan Transfer Learning Untuk Membedakan Tanaman Cabai Berasal Dari Genus Capsicum Annuum Sujaka, Tomi Tri; Switrayana, I Nyoman; Haepa Fillah, Ibnu Mumtaz
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8740

Abstract

Accurate classification of Capsicum annuum varieties is crucial for food industry applications and agricultural research. Traditional manual classification methods are time-consuming, subjective, lack detail, and are prone to human error, requiring computer vision to automate them. This study presents learning in the form of automatic classification of nine diverse Capsicum annuum varieties using transfer learning with the MobileNetV3 architecture, which is designed to achieve high accuracy and be computationally energy efficient. The dataset consists of 4,500 images (training, testing, and validation) of 9 chili varieties: bell pepper, curly chili, cherry pepper, chiltepin, Hungarian wax, jalapeno, marconi, pequin, and Thai chili. This dataset goes through quality control, one of which is dataset balancing. The model in this study has also been optimized with Adam (Adaptive Moment Estimation). Model interpretation is also improved through Grad-CAM visualization, and model robustness has also been validated using cross-validation 5 times. This model achieved performance with a training accuracy of 97.2%, a testing accuracy of 95.1%, and a validation test of 94.8%, where 5-fold cross-validation showed consistent results (94.23% ± 1.45%). Grad-CAM analysis showed that this model focuses on structural features such as shape, surface texture, and color patterns. With the successful development of an AI system that can automatically identify chili varieties with an accuracy of 95.1%. This system works well in real conditions (90.6% accuracy) and is practical for use in agriculture and food processing. This technology can help farmers and food companies or lay people to sort chilies automatically, reduce costs, and improve quality control.
A Multimodal Deep Learning Framework for Amyotrophic Lateral Sclerosis Diagnosis using Clinical and Audio Morphology Features Switrayana, I Nyoman; Sujaka, Tomi Tri; Silpiana Putri, Imelda
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5763

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a highly progressive neurodegenerative disease that impairs motor and speech function. Conventional diagnostic methods, both invasive and non-invasive, are often time-consuming and produce limited sensitivity. This leads to delays in treatment and worsening disease progression. This study proposes a multimodal deep learning framework that utilizes and integrates invasive medical records with non-invasive morphological features of patient speech audio extracted into Mel-Spectrograms. Unlike previous studies that focused solely on speech or clinical features, this study introduces an integrated multimodal diagnostic framework that effectively combines both data sources to achieve reliable diagnostic accuracy. The study included two experimental scenarios. In the first scenario, the audio-trained model used a Convolutional Neural Network (CNN) and was systematically optimized by testing variations in network depth, feature fusion techniques, and layer dropout probabilities to improve model generalization and stability. From the experimental results of the first scenario, the CNN achieved the best performance, achieving 80.33% accuracy in classification using audio data alone from all the tested model variations. In the second experimental scenario, when the best model was trained by incorporating clinical data, the model demonstrated improved diagnostic performance, achieving 100% accuracy. This finding highlights the importance of combining data modalities or sources from various domains, both invasive and non-invasive, to achieve optimal model performance for early ALS detection.
PELATIHAN TEKNOLOGI CERDAS UNTUK MEMULAI BISNIS START UP DAN MANAJEMEN KEUANGAN BISNIS PT. MELUKIS SENYUM INDAHMU I Nyoman Switrayana; L. Jatmiko Jati; Wisnu Alfiansyah; Muhlisin Muhlisin; Mohammad Ziad Anwar; Rini Adriani Auliana
Jurnal Pengabdian kepada Masyarakat Vol. 11 No. 2 (2024): JURNAL PENGABDIAN KEPADA MASYARAKAT 2024
Publisher : P3M Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/abdimas.v11i2.6434

Abstract

The younger generation is creative and ambitious, often channeling this energy into startups. Observations by the community service team at Bumigora University reveal that most students aspire to establish startups to fulfill their needs and gain entrepreneurial experience. However, they face challenges such as limited knowledge of effective business strategies and financial management. This community service activity aims to enhance students' understanding of building and managing startups using smart technologies, particularly Artificial Intelligence (AI). The seminar introduces AI as a tool for developing business strategies, optimizing operations, and automating financial management. The program employs the Asset-Based Community Development (ABCD) method, focusing on leveraging community assets to address their needs. Activities include lectures, interactive sessions, and practical demonstrations to integrate AI into entrepreneurship. Results show a significant improvement in students' knowledge and skills in applying AI for business purposes. The seminar effectively enhanced their understanding of strategies for building businesses, designing marketing plans, and managing finances. Post-test results from participants via Google Form confirm these outcomes. This activity empowers the younger generation to establish competitive, technology-driven businesses.
Co-Authors Abdurahman Abdurrahman Adinda Mutia Gani Afrig Aminuddin Apriani Apriani Arie Cahyani Ashadi, Diki Baiq Rabiatul Adawiyah Kartika Wulan Bukran Bukran Darmawan, I Made Angga Wahyu Diki Ashadi Fitri, Yelli Galuh Tinezia Endriyanto Gozin Najah Rusyada Haepa Fillah, Ibnu Mumtaz Hairani Hairani Hammad, Rifqi I Wayan Bratayasa I Wayan Mustika Nayottama Adi Wijaya Ibrahim, Isra Dewi Kuntary Indah Nalurita Kartika Gemma Pravitri Ketut Widya Kayohana Kurniadin Abd Latif Logi Mulawarman Logi Mulawarman Mohammad Ziad Anwar Muhamad Azwar, Muhamad Muhamad Wisnu Alfiansyah Muhammad Haris Nasri Muhammad Mujahid Dakwah Muhammad Nizhar Naufali Muhammad Tahir Muhammad Tahir Muhlisin Muhlisin Mulawarman, Logi Nabila, Fitri Neny Sulistianingsih Pahrul Irfan Pratama, Gede Yogi Ridha Nurul Hayati Rini Adriani Auliana Rini Anggriani Risyaf Kudus Pranasa Rizal Ramdani rodhi, mohammad najib Rusyada, Gozin Najah Silpiana Putri, Imelda Sirojul Hadi Siti Fatimah Solina, Eva Sujaka, Tomi Tri Susilo Talidobel Syafira Mahfuzi Ardiyati Syahrir, Moch. Tomi Tri Sujaka Wisnu Alfiansyah Zamroni Alpian Muhtarom Zefanya Andryan Girsang